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Eliciting informative user opinions from online reviews is a key success factor for innovative product design and development. The unstructured, noisy, and verbose nature of user reviews, however, often complicate large-scale need finding in a format useful for designers without losing important information. Recent advances in abstractive text summarization have created the opportunity to systematically generate opinion summaries from online reviews to inform the early stages of product design and development. However, two knowledge gaps hinder the applicability of opinion summarization methods in practice. First, there is a lack of formal mechanisms to guide the generative process with respect to different categories of product attributes and user sentiments. Second, the annotated training datasets needed for supervised training of abstractive summarization models are often difficult and costly to create. This article addresses these gaps by (1) devising an efficient computational framework for abstractive opinion summarization guided by specific product attributes and sentiment polarities, and (2) automatically generating a synthetic training dataset that captures various degrees of granularity and polarity. A hierarchical multi-instance attribute-sentiment inference model is developed for assembling a high-quality synthetic dataset, which is utilized to fine-tune a pretrained language model for abstractive summary generation. Numerical experiments conducted on a large dataset scraped from three major e-Commerce retail stores for apparel and footwear products indicate the performance, feasibility, and potentials of the developed framework. Several directions are provided for future exploration in the area of automated opinion summarization for user-centered design. [ABSTRACT FROM AUTHOR]

With the commencement of new technology, demands of online shopping are increasing day by day and hence an electronic product receives a huge number of customers reviews everyday. Because of this, a customer who wants to buy a particular product face difficulty as he needs to go through all the reviews of that product before taking a final decision. Automatically generated summary of the reviews could aid the customers in selecting the appropriate product. Aiming in this direction, a novel approach for making automatic extractive text summaries of the reviews for various electronic products is proposed in this paper. We have taken into account both the content of the review and the author's credibility while evaluating the importance of a sentence. Both the content and semantic similarities are measured between every pair of sentences of a review. In order to form the summary of the reviews, fuzzy c-means clustering is used. For experimental purpose, Amazon dataset is used and the results indicate that the proposed method outperforms some of the baseline methods for generating the summary of the reviews, thus providing more concrete and robust summary. [ABSTRACT FROM AUTHOR]

• We proposed a novel summary method by using code vector of autoencoder. • Unsupervised abstractive summarization using Adversarially regularized autoencoder. • Conditional model is proposed and enhanced performance for review summarization. Abstractive summarization is challenging problem, especially abstractive summarization based on unsupervised learning because it must generate whole, unique sentences. In the real world, companies use abstractive summarization to understand customer feedbacks. In many cases, this work is done by humans and so is expensive in terms of time and money. Therefore, there is an increasing demand for machine learning-based abstractive summarization systems. However, most previous abstractive summarization studies were of supervised models. In this paper, we proposed novel abstractive summarization methods that can be trained unsupervisedly. One of the proposed methods is based on Adversarially Regularized Autoencoder(ARAE) model, but abstractive summary generation method for each cluster of similar customers' reviews, is newly proposed. We further proposed Conditional Adversarially Regularized Autoencoder(CARAE) model which is similar to the ARAE model but with the addition of condition nodes so that additional information about the cluster can be used during summarization. We first performed summary experiments based on Korean and additionally performed experiments on English. In the experiments, we set up some comparison models and used ROUGE and BLEU to evaluate our proposed models' performance. Overall, our proposed models outperformed the comparison models and CARAE model performed better than the ARAE model. [ABSTRACT FROM AUTHOR]